Metis: Selecting Diverse Atlas Vantage Points
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The popularity of the RIPE Atlas measurement platform comes primarily from its openness and unprecedented scale. The platform provides users with over ten thousand vantage points, called probes, and is usually considered as giving a reasonably faithful view of the Internet. A good use of Atlas, however, requires a clear understanding of its limitations and bias. In this work we highlight the influence of probe locations on Atlas measurements and advocate the importance of selecting a diverse set of probes for fair measurements. We propose Metis, a data-driven probe selection method, that picks a diverse set of probes based on topological properties (e.g., round-trip time or AS-path length). Using real experiments we show that, compared to Atlas’ default probe selection, Metis’ probe selections collect more comprehensive measurement results in terms of geographical, topological, RIR, and industry-type coverage. Metis triples the number of probes from the underrepresented AFRINIC and LACNIC regions, and improves geographical diversity by increasing the number of unique countries included in the probe set by up to 59%. In addition, we extend Metis to identify locations on the Internet where new probes would be the most beneficial for improving Atlas’ footprint. Finally, we present a website where we publish periodically updated results and provide easy integration of Metis’ selections with Atlas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it